structure d'un peuplement forestier

A general method for the classification of forest stands using species composition and vertical and horizontal structure / Miquel de Cáceresin Annals of Forest Science [en ligne], vol 76 n° 2 (June 2019)

(Auteur) Context : Forest typologies are useful for many purposes, including forest mapping, assessing habitat quality, studying forest dynamics, or defining sustainable management strategies. Quantitative typologies meant for forestry applications normally focus on horizontal and vertical structure of forest plots as main classification criteria, with species composition often playing a secondary role. The selection of relevant variables is often idiosyncratic and influenced by a priori expectations of the forest types to be distinguished.
Aims : We present a general framework to define forest typologies where the dissimilarity between forest stands is assessed using coefficients that integrate the information of species composition with the univariate distribution of tree diameters or heights or the bivariate distribution of tree diameters and heights.
Methods : We illustrate our proposal with the classification of forest inventory plots in Catalonia (NE Spain), comparing the results obtained using the bivariate distribution of diameters and heights to those obtained using either tree heights or tree diameters only.
Results : The number of subtypes obtained using the tree diameter distribution for the calculation of dissimilarity was often the same as those obtained from the tree height distribution or to those using the bivariate distribution. However, classifications obtained using the three approaches were often different in terms of forest plot membership.
Conclusion : The proposed classification framework is particularly suited to define forest typologies from forest inventory data and allows taking advantage of the bivariate distribution of diameters and heights if both variables are measured. It can provide support to the development of typologies in situations where fine-scale variability of topographic, climatic, and legacy management factors leads to fine-scale variation in forest structure and composition, including uneven-aged and mixed stands.

(auteur) Recently, many studies have found positive biodiversity–productivity relationships in forests. In contrast, different types of correlations have been identified in the analyses of tree diversity–structure–productivity relationships. We suspect that these conflicting conclusions might result from the different developmental stages of the investigated forest stands. We therefore analyzed the development of tree diversity–structure–productivity relationships at the stand level and individual tree level in 192 long-term experimental plots in Central Europe. As a measure of stand productivity, we analyzed stand volume growth (m3 ha−1 year−1). Tree species diversity was quantified by the Shannon index and structural heterogeneity was represented by the Gini coefficient of basal area. For a more detailed analysis at the tree level using a smaller portion of the dataset, the tree position–dependent indices, diameter differentiation index, and aggregation index were used. Whether the effect of structural heterogeneity on stand productivity was positive or negative depended on the stand development stage. In early developmental stages, high structural heterogeneity lowered productivity. In later developmental stages, however, stand structural heterogeneity had a positive effect on productivity. Our study might provide insights regarding the mechanisms underlying the contradictory findings obtained in recent studies dealing with tree diversity–structure–productivity relationships. This knowledge is vital for the adaptation of forest management to meet future demands on forest ecosystems.

(auteur) Successful restoration of degraded forest landscapes requires reference models that adequately capture structural heterogeneity at multiple spatial scales and for specific landforms. Despite this need, managers often lack access to reliable reference information, in large part because field-based methods for assessing variation in forest structure are costly and inherently suffer from limited replication and spatial coverage and, therefore, yield limited insights about the ecological structure of reference forests at landscape scales. LiDAR is a cost-effective alternative that can provide high-resolution characterizations of variation in forest structure among landform types. However, managers and researchers have been reluctant to use LiDAR for characterizing structure because of low confidence in its capacity to approximate actual tree distributions. By calculating bias in LiDAR estimates for a range of tree-height cutoffs, we improved LiDAR’s ability to capture structural variability in terms of individual trees. We assessed bias in the processed LiDAR data by comparing datasets of field-measured and LiDAR-detected trees of various height classes in terms of overall number of trees and estimates of structure and spatial pattern in an important contemporary reference forest, the Sierra de San Pedro Martir National Park, Baja California, Mexico. Agreement between LiDAR- and field-based estimates of tree density, as well as estimates of forest structure and spatial pattern, was maximized by removing trees less than 12 m tall. We applied this height cutoff to LiDAR-detected trees of our study landscape, and asked if forest structure and spatial pattern varied across topographic settings. We found that canyons, shallow northerly, and shallow southerly slopes were structurally similar; each had a greater number of all trees, large trees, and large tree clumps than steep southerly slopes and ridges. Steep northerly slopes supported unique structures, with taller trees than ridges and shorter trees than canyons and shallow southerly slopes. Our results show that characterizations of forest structure based on LiDAR-detected trees are reasonably accurate when the focus is narrowed to the overstory. In addition, our finding of strong variation of forest structure and spatial pattern across topographic settings demonstrates the importance of developing reference models at the landscape scale, and highlights the need for replicated sampling among stands and landforms. Methods developed here should be useful to managers interested in using LiDAR to characterize distributions of medium and large overstory trees, particularly for the development of landscape-scale reference models.

(auteur) Forest processes that play an essential role in carbon sequestration, such as light use efficiency, photosynthetic capacity, and trace gas exchange, are closely tied to the three-dimensional structure of forest canopies. However, the vertical distribution of leaf traits is not uniform; leaves at varying vertical positions within the canopy are physiologically unique due to differing light and environmental conditions, which leads to higher carbon storage than if light conditions were constant throughout the canopy. Due to this within-canopy variation, three-dimensional structural traits are critical to improving our estimates of global carbon cycling and storage by Earth system models and to better understanding the effects of disturbances on carbon sequestration in forested ecosystems. In this study, we describe a reproducible and open-source methodology using the R programming language for estimating leaf area density (LAD; the total leaf area per unit of volume) from airborne LiDAR. Using this approach, we compare LAD estimates at the Smithsonian Environmental Research Center in Maryland, USA, from two airborne LiDAR systems, NEON AOP and NASA G-LiHT, which differ in survey and instrument specifications, collections goals, and laser pulse densities. Furthermore, we address the impacts of the spatial scale of analysis as well as differences in canopy penetration and pulse density on LAD and leaf area index (LAI) estimates, while offering potential solutions to enhance the accuracy of these estimates. LAD estimates from airborne LiDAR can be used to describe the three-dimensional structure of forests across entire landscapes. This information can help inform forest management and conservation decisions related to the estimation of aboveground biomass and productivity, the response of forests to large-scale disturbances, the impacts of drought on forest health, the conservation of bird habitat, as well as a host of other important forest processes and responses.

(auteur) Reliable assessment of forest structural types (FSTs) aids sustainable forest management. We developed a methodology for the identification of FSTs using airborne laser scanning (ALS), and demonstrate its generality by applying it to forests from Boreal, Mediterranean and Atlantic biogeographical regions. First, hierarchal clustering analysis (HCA) was applied and clusters (FSTs) were determined in coniferous and deciduous forests using four forest structural variables obtained from forest inventory data – quadratic mean diameter , Gini coefficient , basal area larger than mean and density of stems –. Then, classification and regression tree analysis (CART) were used to extract the empirical threshold values for discriminating those clusters. Based on the classification trees, and were the most important variables in the identification of FSTs. Lower, medium and high values of and characterize single storey FSTs, multi-layered FSTs and exponentially decreasing size distributions (reversed J), respectively. Within each of these main FST groups, we also identified young/mature and sparse/dense subtypes using and . Then we used similar structural predictors derived from ALS – maximum height (), L-coefficient of variation (), L-skewness (), and percentage of penetration (), – and a nearest neighbour method to predict the FSTs. We obtained a greater overall accuracy in deciduous forest (0.87) as compared to the coniferous forest (0.72). Our methodology proves the usefulness of ALS data for structural heterogeneity assessment of forests across biogeographical regions. Our simple two-tier approach to FST classification paves the way toward transnational assessments of forest structure across bioregions.